Learning with group invariant features: A Kernel perspective
We analyze in this paper a random feature map based on a theory of invariance (I-theory) introduced in [1]. More specifically, a group invariant signal signature is obtained through cumulative distributions of group-transformed random projections. Our analysis bridges invariant feature learning with...
Main Authors: | , , |
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Other Authors: | |
Format: | Article |
Language: | English |
Published: |
Association for Computing Machinery,
2017-11-28T19:15:40Z.
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Subjects: | |
Online Access: | Get fulltext |